Image processing apparatus and method

Abstract: In converting image sensing data into image data, a plurality of image sensing data meeting a predetermined condition are processed as a group of image sensing data. An image reproduction parameter is obtained from this image sensing data group, and each image sensing data of the image sensing data group is converted into image data by using the image reproduction parameter. Accordingly, an image reproduction parameter for obtaining an optimum reproduced image can be accurately set from the image sensing data group. Also, since a reproduction luminance level (range) is determined from the image sensing data group, the correlation between the luminances of image planes is not lost. This allows an easy comparison of reproduced images and prevents the boundaries of luminances from becoming unnatural when the reproduced images are synthesized.

Description: BACKGROUND OF THE INVENTION Field of the Invention: The present invention relates to an image processing apparatus and method and, more particularly, to an image processing apparatus and method of adjusting the hue of an input image signal. The present invention also relates to an image reproducing method and apparatus and, more particularly, to an image reproducing method and apparatus for converting an image sensing signal obtained from an image sensing device, such as an image sensing tube or a CCD, into a visualizable image signal, e.g., an NTSC-RGB signal. Description of the Related Art: In a television camera using an image sensing device such as a CCD, some image reproduction parameters are generally determined from image sensing data during image reproduction processing, in order to constantly obtain images which apparently give the same impression or to obtain as faithful reproduced images as possible regardless of deterioration with time of the image sensing device or a color filter and changes in an illuminating light source. The image sensing data is two-dimensional digital image data formed from an image signal obtained by photographing an object by using an image sensing device. Examples of the image reproduction parameters are a color temperature and a reproduction luminance level. The image production parameters are used to correct the color temperature or set the reproduction luminance level. More specifically, the correction of the color temperature is to adjust a so-called white balance so that an object which is supposed to look white looks white. Generally, this color temperature correction is performed on the basis of image sensing data. That is, data of an object which is supposed to look white is extracted from image sensing data, and a white balance coefficient as one image reproduction parameter is determined on the basis of the extracted data. In the white balance adjustment, a plurality of color component signals constituting an output image signal from an image sensing device are amplified in accordance with the white balance coefficient. Consequently, the signal levels of the color components constituting the image signal of the object which is supposed to look white are so adjusted as to be equal to each other. The setting of the reproduction luminance level is done by calculating a luminance distribution from image sensing data and setting an optimum reproduction luminance level (range). The parameter is adjusted such that a reproduced image is obtained within this range, and the image is reproduced. Figs. 1 and 2 are block diagrams showing configurations for performing the color temperature correction. Referring to Fig. 1, complementary color data (consisting of color component signals of magenta Ma, green Gr, Yellow Ye, and cyan Cy) obtained by an image sensing unit 1 is supplied to a complementary color-pure color converting unit 11. The complementary color data is converted into pure color data (consisting of color component signals of red R, green G, and blue B) in the converting unit 11. The white balance of the pure color data obtained by the complementary color-pure color converting unit 11 is adjusted by a white balance (WB) adjusting unit 12 in the subsequent stage, and the gamma of the data is corrected by a gamma correcting unit 4. In the configuration shown in Fig. 1 as above, the WB adjusting unit 12 is arranged subsequently to the complementary color-pure color converting unit 11, and the color temperature correction is done by performing the white balance adjustment for the pure color data (R,G,B) after complementary colors are converted into pure colors. This configuration is advantageous in that the color temperature correction can be relatively easily performed because the gain of the pure color data (R,G,B) can be directly adjusted. In the configuration shown in Fig. 2, on the other hand, a WB adjusting unit 2 adjusts the white balance of complementary color data (Ma,Gr,Ye,Cy) obtained by an image sensing unit 1. Thereafter, a complementary color-pure color converting unit 3 performs complementary color-pure color conversion to obtain pure color data (R,G,B). This configuration has the advantage that a luminance signal with a higher resolution than that obtained in the configuration shown in Fig. 1 can be easily obtained. The method of adjusting the hue of an image by adjusting the white balance is effective when many objects which are supposed to look white exist in an image signal obtained from an image sensing device. However, no such object which is supposed to look white exists in an image signal or only a very few such objects exist in an image signal in specific instances. In these instances, therefore, it is in principle impossible to adjust the hue by adjusting the white balance. In such instances, the general approach is to average image sensing data of one image plane for each color component and adjust the white balance by using the average. However, a color indicated by the obtained average is not necessarily white (the color of a light source), and so the white balance cannot be accurately adjusted. That is, the white balance coefficient cannot be accurately set if it is determined from image sensing data in order to obtain an optimum reproduced image. Also, in the setting of the reproduction luminance level, if the reproduction luminance level (range) is determined for each image plane, the correlation between the luminances of a plurality of image planes is lost. This makes the comparison of reproduced images difficult, or the correction of luminances becomes unnatural when the reproduced images are synthesized. For example, the above inconveniences are significant when an object which is to be originally, desirably photographed as one image plane is divisionally photographed because the photographing area is small and one image plane is formed by synthesizing image sensing data of the obtained image planes. That is, in the method of obtaining an image production parameter for each image sensing data of one image plane, it is impossible to obtain a reproduced image which is used when information between a plurality of images is extracted by comparing and analyzing the images, such as when physical property information is obtained from luminance information. Also, if the reflectance of an object spatially, gradually changes, individual image sensing data obtained by divisionally photographing the object have different luminance levels (ranges). If images are reproduced by independently optimizing these image sensing data, the correlation between luminances originally corresponding to the respective image sensing areas is lost in the reproduced images. Accordingly, if one image is formed by synthesizing these images taken in the respective image sensing areas, an unnatural synthetic image in which the correlation between luminances is lost results. The hue of an image is adjusted by adjusting the white balance as follows. An object which is supposed to look white under a certain photographing light source is photographed. The amplification factor of each of a plurality of color component signals constituting an image signal obtained from the image sensing device is so adjusted that the white object accurately looks white when the image signal is reproduced. That is, it can be considered that the white balance adjustment is performed to compensate for changes in the light source during photography. Commonly, the white balance adjustment described above is a principal means for compensating for changes in the light source during photography. A white balance coefficient used in this white balance adjustment is obtained on the basis of information of the light source during photography. Of a plurality of different image reproduction parameters used in image reproduction, some parameters are preferably obtained on the basis of information of the light source during photography, like the image reproduction parameter (white balance coefficient) used in the white balance adjustment. An example is a complementary color-pure color conversion matrix used to convert an image signal obtained by using a complementary color filter into a pure color signal. The complementary color-pure color conversion matrix is determined by the spectral transmittance characteristic of a complementary color filter. Usually, the spectral transmittance characteristic of a complementary color filter is not ideal. The influence of this difference from the ideal characteristic changes in accordance with the characteristics of the light source during photography. That is, a complementary color-pure color conversion matrix optimally selected under a certain photographing light source gives an optimum complementary color-pure color conversion result under this light source. However, this matrix does not give suitable conversion results to all light sources. When a photographing light source changes, therefore, it is desirable to change the complementary color-pure color conversion matrix in accordance with the light source. Also, the above two image reproduction parameters, i.e., the white balance coefficient and the complementary color-pure color conversion matrix, are related to each other under a certain photographing light source. Accordingly, it is undesirable to individually determine these parameters. Generally, however, the complementary color-pure color conversion is performed by using a semi-fixed complementary color-pure color conversion matrix which is optimally set under a certain photographing light source. If the photographing light source changes, therefore, the influence of the difference of the spectral transmittance characteristic of a complementary color filter from the ideal characteristic increases. Also, a contradiction sometimes occurs between the white balance coefficient and the complementary color-pure color conversion matrix having the correlation. Consequently, no complementary color-pure color conversion can be properly performed, and this makes faithful reproduction of an image difficult. The present invention has been made in view of the above conventional problems, and has as a concern to provide an image processing apparatus and method capable of accurately setting, from a group of image sensing data, an image reproduction parameter for obtaining an optimum reproduced image. European Patent Specification No EP-A-0502369 discloses method and apparatus for processing an image by setting out image processing conditions based on image data obtained by scanning image information recorded on an original to read. The scanned image is displayed and an operator designates particular spots on the image so as to set image processing conditions for a subsequent main scan. European Patent Specification No EP-A-0363988 discloses a color scanner for reading an image stored in an original cassette by carrying out either rough scanning or main scanning. In this prior document an image processing condition for a main scan corresponding to an original image is automatically calculated from image data obtained by the pre-scan. Accordingly in a first aspect the invention provides an image processing apparatus as set out in claim 1. In a second aspect the invention provides an image processing method as set out in claim 8. Other features and advantages of the present invention will be apparent from the following description taken in conjunction with the accompanying drawings, in which like reference characters designate the same or similar parts throughout the figures thereof. BRIEF DESCRIPTION OF THE DRAWINGS Figs. 1 and 2 are block diagrams showing configurations for performing color temperature correction; Fig. 3 is a block diagram showing the configuration of an image reproducing apparatus according to the first embodiment of the present invention; Fig. 4 is a flow chart showing the procedure of image processing done by the image processing apparatus of the first embodiment; Fig. 5 is a view for explaining an example of a synthetic image; Fig. 6 is a view for explaining composition information of an object; Fig. 7 is a view for explaining image sensing data having a time continuity; Fig. 8 is a flow chart showing image sensing data grouping processing; Fig. 9 shows an example of a chromaticity diagram; Fig. 10 is a view showing the range of a correlated color temperature of image sensing data represented by (R/G,B/G) signal values; Fig. 11 is a flow chart showing image sensing data grouping processing when a composition is designated as the condition of grouping; and Fig. 12 is a flow chart showing processing of grouping image sensing data by using information appended to the image sensing data; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiments of the present invention will be described below with reference to the accompanying drawings. <First Embodiment> [Structure] Fig. 3 is a block diagram showing an embodiment of an image reproducing apparatus according to the present invention. A color image sensing unit 10 such as a digital camera senses the image of an object and outputs the image sensing data of the object to an image sensing data memory 20. The image sensing data memory 20 stores the image sensing data supplied from the color image sensing unit 10. A color image reproduction processor 30 performs predetermined image reproduction processing for the image sensing data stored in the image sensing data memory 20. For example, the color image reproduction processor 30 converts the image sensing data into digital NTSC-RGB data and outputs the digital data to a color image reproducing display 40 and a reproduced image data memory 50. The color image reproducing display 40 includes a color video card and a monitor. The color image reproducing display 40 receives an output color image signal from the color image reproduction processor 30 or reads out a color image signal from the reproduced image data memory 50 and displays the signal as a color image on the monitor. The reproduced image data memory 50 stores the image data reproduced by the color image reproduction processor 30. The image data stored in the reproduced image data memory 50 is supplied to and displayed by the color image reproducing display 40 as needed. More specifically, the color image reproduction processor 30 comprises an image sensing data holding unit 31, an image sensing data selecting unit 33, an image sensing data selection designating unit 34, an image reproduction parameter determining unit 32, an image reproduction processing unit 35, and a control unit 36. The image sensing data holding unit 31 holds the digital image sensing data from the image sensing data memory 20. The image sensing data selecting unit 33 selects image sensing data from the image sensing data memory 20 and outputs the selected data to the image sensing data holding unit 31. The image sensing data selection designating unit 34 designates the condition by which the image sensing data selecting unit 33 selects image sensing data. The image reproduction parameter determining unit 32 determines an image reproduction parameter by using the image sensing data held by the image sensing data holding unit 31. The image reproduction processing unit 35 reproduces an image of the image sensing data held by the image sensing data holding unit 31 by using the image reproduction parameter determined by the image reproduction parameter determining unit 32. The control unit 36 controls these units of the color image reproduction processor 30. The control unit 36 is constituted by, e.g., one-chip microcontroller (MPU) and executes various processes (to be described later) in accordance with programs previously stored in an internal ROM 36a. An internal RAM 36b of the MPU is used as a work memory of the MPU. Examples of the image reproduction parameter determined by the image reproduction parameter determining unit 32 are parameters indicating a color temperature, a white balance coefficient, a color component gain, white point information, black point information, a gamma coefficient, a gradation characteristic, a gradation conversion curve, a gradation conversion lookup table, a knee point, a dynamic range, a color gamut, light source information, a color coordinate conversion matrix coefficient, a spatial frequency characteristic, a black balance coefficient, an S/N ratio, an auto-correlation coefficient, a Wiener spectrum, an intensity (density) distribution, and a luminance distribution, and parameters obtained directly or indirectly from these pieces of information. The image sensing data selection designating unit 34 can designate the condition of selection in order that of a plurality of image sensing data stored in the image sensing data memory 20, image sensing data meeting a predetermined condition be processed as a group of image sensing data. The designated condition is given as a condition related to color temperature information or the composition of an object obtained from image sensing data, or as a predetermined condition pertaining to the photographing time, the photographing mode, the photographing place, the EV value, the aperture value, the object distance, the shutter speed, the use/nonuse of an electronic flash, or the use/nonuse of an optical low-pass filter, each of which is information appended to image sensing data. [Image Reproduction Processing] Fig. 4 is a flow chart showing the procedure of image processing done by the image reproducing apparatus of this embodiment. Fig.4 shows processing executed by the color image reproduction processor 30. Assume that a plurality of image sensing data obtained by the color image sensing unit 10 are already stored in the image sensing data memory 20. When the processing shown in Fig. 4 starts, the control unit 36 for controlling the processing checks the contents of image sensing data selection designated (in step S1) by the image sensing data selection designating unit 34 and sends the contents of the designated selection to the image sensing data selecting unit 33. On the basis of the contents of the selection designated by the image sensing data selection designating unit 34, the image sensing data selecting unit 33 selects image sensing data meeting the contents of the designated selection from the image sensing data stored in the image sensing data memory 20 and outputs the selected data to the image sensing data holding unit 31 (step S2). If the image sensing data meeting the contents of the designated selection is data of less than one image plane, this processing is not executed. If the selected image sensing data is data of one image plane, the image sensing data is converted into image data by ordinary image reproduction processing. If there is no image sensing data to be selected, information indicating this is output, and the processing is terminated. The image sensing data holding unit 31 holds the group of image sensing data selectively output from the image sensing data memory 20 (step S3). The image reproduction parameter determining unit 32 analyzes the image sensing data group held by the image sensing data holding unit 31, obtains, e.g., light source information (e.g., the color temperature or the chromaticity of the light source) when the image sensing data are obtained, which is necessary in white balance adjustment, and determines an image reproduction parameter on the basis of the light source information (step S4). That is, by using the plurality of image sensing data selectively output as a group of image sensing data from the image sensing data memory 20 and held in the image sensing data holding unit 31, the image reproduction parameter determining unit 32 determines an image reproduction parameter and sends the parameter to the image reproduction processing unit 35. By using the image reproduction parameter thus determined, the image reproduction processing unit 35 performs image reproduction processing by which the image sensing data group held by the image sensing data holding unit 31 is converted into a group of image data (step S5). [Selection Conditions of Image Sensing Data] The conditions under which image sensing data are selected from a plurality of image sensing data so as to be processed as one group of image sensing data will be described below. Information obtained from the analysis of image sensing data itself includes-color temperature information and the composition of an object. It is sometimes necessary to process a plurality of image sensing data whose color temperatures are within a certain fixed range as a group of correlated image sensing data. Especially when the number of samples used to estimate color temperatures is small, the estimation accuracy can be increased by again estimating the color temperatures by using a plurality of image sensing data whose estimated color temperatures are within a certain fixed range. Consequently, image reproduction can be performed with a high accuracy. If the compositions of objects are the same or very similar, there is a high possibility that the objects were photographed under the same photographing condition. Accordingly, the image reproduction parameters of the images are the same or can be processed as the same parameter within a certain fixed range of accuracy. If this is the case, an image reproduction parameter can be accurately obtained when it is obtained from a plurality of image sensing data. It is in many instances useful to process image sensing data having the same object composition or similar object compositions as a group of image sensing data. Examples of information useful to find the correlation between image sensing data are the photographing time (date), the photographing mode, the photographing place, the EV value, the shutter speed, the aperture value, the object distance, the use/nonuse of an electronic flash, and the use/nonuse of an optical low-pass filter. If these pieces of information are given as information appended to image sensing data, a group of image sensing data can be generated by analyzing these pieces of information. If the photographing times of image sensing data are within a certain fixed range, it is obviously effective to process these data as a group of image sensing data. It is also clearly effective to process a series of image sensing data whose imaging mode is a continuous shooting mode as a group of image sensing data. When an electronic flash is used, information of the photographing light source largely depends upon the characteristics of the flash light. Therefore, it is useful to simultaneously process a plurality of image sensing data, obtained by using an electronic flash, as a group of image sensing data. Pieces of information such as the EV value, the shutter speed, the aperture value, and the object distance extremely largely depend upon the photographing conditions and function as strong restricting conditions when a reproduction parameter is determined. Therefore, image sensing data having these values within a predetermined range are processed as one image sensing data group. This is very efficient when image reproduction is performed. In information of the photographing place, the characteristics of an object remain unchanged. Therefore, this information is very effective in accurately reproducing an object. Additionally, when this information is used in combination with information of, e.g., the photographing time (date), information on a change in the photographing conditions can also be obtained. This obviously increases the usefulness of the information of the photographing place. In the above example, the condition for each individual information is primarily described. However, it is evident that the effectiveness is sometimes greatly improved by combining the above conditions. As shown in Fig.5, for example, as the condition of image reproduction processing, a plurality of image sensing data having a spatial continuity are sometimes designated to obtain a synthetic image. If this is the case, it is necessary to hold the spatial continuity in the synthetic image. For this purpose, it is very effective to perform image reproduction processing by processing a plurality of image sensing data as one group of image sensing data. Also, as illustrated in Fig. 6, if it can be determined from photographing place information or composition information obtained by analyzing image sensing data that the image sensing data have a time continuity and are obtained from objects which are nearly the same, a reproduced image maintaining the time continuity can be obtained by processing these image sensing data as one group of image sensing data. If these image sensing data are obtained outdoors, for example, an image can be reproduced while a continuous change with a change in the photographing time, i.e., the condition of sunshine, is held. More specifically, a reproduced image having a time continuity can be obtained by continuously changing an image reproduction parameter of image sensing data of an image sensing data group consisting of a plurality of image sensing data obtained from objects which are almost the same. This allows reproduction of an image while correcting disturbance occurring in some image sensing data due to a sudden change in the conditions. As a consequence, a temporally natural image can be obtained. Also, as shown in Fig. 7, image sensing data having both a time continuity and a continuity in the photographing order, i.e., image sensing data obtained when the photographing mode is a continuous shooting mode, or image sensing data also having a spatial continuity, are processed as one group of image sensing data. Consequently, an extraneous condition occurring in each individual image sensing data can be eliminated, and this allows reproduction of an image keeping the continuity. [Grouping of Image Sensing Data] Grouping Using Color Temperature Information A method of grouping image sensing data, i.e., a method of obtaining a group of image sensing data, by using color temperature information obtained from the analysis of image sensing data will be described in detail below. Fig. 8 is a flow chart showing the image sensing data grouping processing. The processing is executed by the control unit 36 by controlling the image sensing data selection designating unit 34 and the image sensing data selecting unit 33. The conditions given by the image sensing data selection designating unit 34 are a correlated color temperature (A(K)) and its selecting range color temperature (±dA(K)). The correlated color temperature range is sometimes directly input by the user as a numerical value. It is also possible to input information related to the correlated color temperature, e.g., words such as "daytime", "evening", "sunset glow", "cloud", and "incandescent lamp", which the use can imagine in relation to the color temperature. That is, color temperature ranges corresponding to these words can be previously defined and stored in the ROM 36a, and a color temperature range corresponding to the information input by the user can be read out and used. Alternatively, the color temperature ranges corresponding to these words can be obtained by calculations (step S11 in Fig. 8). It is also possible to use adjectives such as "red" and "pale" from which the color temperatures can be imagined or practical values of a chromaticity or a color difference. When the above conditions are designated, the image sensing data selection designating unit 34 designates the correlated color temperature as the intrinsic information of image sensing data by using, e.g., a chromaticity diagram shown in Fig.9. Fig. 9 is a graph shown as Appended Figure 1 in JIS Z 8725 "Methods for Determining Distribution Temperature and Color Temperature or Correlated Color Temperature of Light Sources". In Fig. 9, a reciprocal correlated color temperature T CP -1 has the following relationship with a correlated color temperature T CP : T CP (K) = 10 6 T CP -1 (MK -1 ) The designated correlated color temperature is supplied to the image sensing data selecting unit 33 and used as the condition of grouping. The image sensing data selecting unit 33 sequentially loads the image sensing data stored in the image data memory 20 (step S12), analyzes the image sensing data and extracts information pertaining to the color temperature (step S13), and checks whether the color temperature information satisfies the previously designated grouping condition (in this case the correlated color temperature) (step S14). If the color temperature information satisfies the condition, the image sensing data selecting unit 33 supplies the corresponding image sensing data to the image sensing data holding unit 31 (step S15). Generally, the color temperature information obtained by analyzing the image sensing data is not a correlated color temperature itself but another signal having a one-to-one correspondence with the correlated color temperature. For example, when an image sensing device from which an R,G,B signal is obtained is used, it is possible to use, as the signal pertaining to the color temperature information, an (R/G,B/G) signal obtained by averaging image sensing data of one image plane or an (R/G,B/G) signal obtained as an average value of regions (which meet the condition that, e.g., their chromaticity values are within the range which defines the correlated color temperature in Fig. 9) sampled from image sensing data and corresponding to white. Since the (R/G,B/G) signal depends upon the sensitivity characteristic of the image sensing device used with respect to each color component, it is necessary to obtain the correspondence to the correlated color temperature by taking into account the characteristics of an image sensing device used as a system. Since the characteristics of the (R/G,B/G) signals and the correlated color temperatures whose correspondence is thus obtained are in a one-to-one correspondence with each other, the relationship between the correlated color temperature (A(K)) and its selecting range color temperature (±dA(K)) which are the grouping conditions previously given can also be defined as follows by using the (R/G,B/G) signal. That is, the range of the correlated color temperatures of image sensing data to be grouped can be defined as A±dA(K), or, as illustrated in Fig. 10, ((R/G) 0 ±d(R/G) 0 , (B/G) 0 ±d(B/G) 0 ). Accordingly, to directly check by using the (R/G,B/G) signal whether the grouping conditions are met, it is only necessary to check whether the color temperature information (R/G,B/G) obtained by analyzing the image sensing data is within the range of ((R/G) 0 ±d(R/G) 0 , (B/G) 0 ±d(B/G) 0 ). To make this determination by using the correlated color temperature, a correlated color temperature corresponding to the (R/G/,B/G) signal directly obtained from the image sensing data is obtained, and whether this correlated color temperature is within the range of A±dA(K) as the grouping condition is checked. When a condition related to the color temperature information is given as the grouping condition, the above processing is sequentially executed for the image sensing data stored in the image data memory 20. It is determined that image sensing data meeting the grouping condition are image sensing data constituting the group. The image sensing data thus found to constitute the group are supplied to the image sensing data holding unit 31 (step S15) and held in it. The image sensing data selecting unit 33 repeats the processes from step S12 to step S15 until it is determined in step S16 that all of the image sensing data stored in the image data memory 20 are read out. Grouping Using Composition of Object A method of grouping image sensing data when the composition of an object is designated as the condition of grouping will be described below. Fig. 11 is a flow chart showing the image sensing data grouping processing when the composition is designated as the grouping condition. This processing is executed by the control unit 36. If the composition is designated as the grouping condition in step S21, the image sensing data selecting unit 33 loads the image sensing data stored in the image data memory 20 (step S22). The image reproduction processing unit 35 performs simplified reproduction to the extent to which the composition is known (step S23), thereby obtaining simple reproduced image data. The simplified reproduction can be reproduction processing to the extent to which the composition can be known. For example, pixels are thinned from image sensing data and luminance data is reproduced while the data amount is thus decreased. Subsequently, the determination of grouping is performed by using the simple reproduced image data. However, it is generally efficient to subjectively determine, e.g., the similarity between images. Therefore, this subjective determination method is used in this embodiment. That is, if an instruction from the user can be expected in the determination of grouping, a series of simple reproduced image data are displayed on a monitor (steps S24 and S25), and the user selects images to be grouped from the image sensing data selection designating unit 34 (step S26). Image sensing data corresponding to the images selected by the user are sequentially supplied to the image sensing data holding unit 31 (step S30) and held in it. To automatically determine grouping, edge image data is formed by extracting an edge component from simple reproduced image data (step S27), and the sameness or the similarity of the spatial relationship between edges is detected by applying an image processing technology (step S28). The spatial correlation is checked by analyzing the sameness or the similarity, and it is determined that images having a strong correlation are images meeting the grouping condition (step S29). Image sensing data corresponding to the images are supplied to the image sensing data holding unit 31 and held in it (step S30). Grouping Using Appended information A method of grouping image sensing data by using information appended to the image sensing data will be described below. Fig. 12 is a flow chart showing the processing of grouping image data by using information appended to the image sensing data. In step S31, information appended to image sensing data and the condition of the information are given as the conditions of group to the image sensing data selection designating unit 34. The image sensing data selecting unit 33 loads the image sensing data stored in the image data memory 20 (step S32), checks whether the information corresponding to the conditions is appended to the data (step S33), and sequentially loads image sensing data to which the information is appended (step S32). The image sensing data selecting unit 33 then checks whether the appended information meets the designated condition of selection for grouping (step S34). If information such as the photographing time, the EV value, the aperture value, the object distance, or the shutter speed whose range can be numerically set is used, the grouping selection condition is whether the value of the appended information falls within the numerically indicated range. If information such as the image sensing mode, the photographing place, the use/nonuse of an electronic flash, or the use/nonuse of an optical low-pass filter which is difficult to numerically indicate is used, the agreement between the names of the places, the names of the modes, or the states of the use/nonuse is the condition. If grouping information (the same mark is given to image sensing data obtained in the same place) depending on the image sensing mode is given as appended information, the agreement between the given marks is the condition. Image sensing data meeting the grouping selection condition are sequentially supplied to the image sensing data holding unit 31 (step S35) and held in it. The image sensing data selecting unit 33 repeats the processes from step S32 to S35 until it is determined in step S36 that all of the image sensing data stored in the image data memory 20 are loaded. Grouping in Image Synthesis A method of grouping image sensing data when a synthetic image is to be obtained by increasing the dynamic range as disclosed in Japanese Patent Laid-Open No. JP-A-7-131796 or a synthetic image is to be obtained by a method of panorama image synthesis for obtaining a wide-angle image from a plurality of image sensing data as disclosed in Japanese Patent Laid-Open No. JP-A-5-183789 will be described below. In a case like this, it is desirable that appended information indicating that data is image sensing data for obtaining a certain synthetic image be given to the data during photography. If this is the case, in this embodiment image sensing data constituting an image sensing data group are selected and grouped on the basis of the appended information. Alternatively, as explained in "Grouping Using Composition of Object", it is also possible to simply reproduce image sensing data, perform grouping determination, and group the image sensing data. In processing for obtaining a synthetic image, all image sensing data stored in the image data memory 20 are in some cases previously selected as image sensing data to be processed as a group when the processing is executed. If this is the case, in this embodiment, the image reproduction is performed by processing all of the image sensing data stored in the image data memory 20 as a group of image sensing data. In this embodiment as described above, when image sensing data is converted into image data, a plurality of image sensing data meeting predetermined conditions are processed as a group of image sensing data, and an image reproduction parameter is obtained from this image sensing data group. The image sensing data belonging to the image sensing data group can be converted into image data by using this image reproduction parameter. In this embodiment, therefore, when an image reproduction parameter such as a white balance coefficient is determined from image sensing data in order to obtain an optimum reproduced image, this image reproduction parameter can be accurately set. Also, since a reproduction luminance level (range) is determined from a group of image sensing data, the correlation between the luminances of a plurality of image planes is not lost. This makes the comparison of reproduced images possible and prevents the connection of luminances from becoming unnatural when these reproduced images are synthesized.

Claims: An image processing apparatus for reproducing a picture from image data obtained by image sensing means, comprising: selecting means (33) for selecting plural pictures, which are stored in a memory (20) based on a selection condition designated by a user; holding means (31) for holding the plural pictures selected by said selecting means; setting means (32) for setting an image reproduction parameter for adjusting hue or luminance by analysing the plural pictures held by said holding means; and generating means (35) for generating image signals for reproducing the plural pictures by using the image reproduction parameter set by said setting means. Apparatus according to claim 1, wherein the selection condition is a predetermined color temperature range. Apparatus according to claim 1, wherein the selection condition is the composition of the pictures. Apparatus according to claim 3, further comprising reproducing means (35) for reproducing a picture to check the compositions of the plural pictures. Apparatus according to claim 1, wherein the selection condition includes at least one condition selected from a photographing time, a photographing date, a photographing place, a photographing mode, an EV value, a shutter speed, an aperture value, an object distance, the use/non-use of an electronic flash, and the use/non-use of an optical filter. Apparatus according to any preceding claim, wherein said selecting means is adapted to select the plural picture s so that they can be used in image synthesis. Apparatus according to claim 6, wherein said selecting means selects the plural pictures for synthesising them on the basis of information added to each picture. An image processing method for reproducing a picture from image data obtained by image sensing means comprising the steps of: selecting plural pictures, which are stored in a memory, based on a selection condition designated by a user, and causing holding means to hold the selected plural pictures; setting an image reproduction parameter for adjusting hue or luminance by analysing the plural pictures held by the holding means; and generating image signals for reproducing the plural pictures by using the image reproduction parameter set in the setting step.

Inventors: HIDAKA YUMIKO, IKEDA EIICHIRO, SHIRAIWA YOSHINOBU, TAKAHASHI KENJI, UDAGAWA YOSHIRO, HIDAKA, YUMIKO, IKEDA, EIICHIRO, SHIRAIWA, YOSHINOBU, TAKAHASHI, KENJI, UDAGAWA, YOSHIRO, Hidaka, Yumiko, c/o Canon K.K., Ikeda, Eiichiro, c/o Canon K.K., Shiraiwa, Yoshinobu, c/o Canon K.K., Takahashi, Kenji, c/o Canon K.K., Udagawa, Yoshiro, c/o Canon K.K.
Applicants: CANON KK, CANON KABUSHIKI KAISHA
Application year: 1996
Main classification: H04N1
IPC codes: H04N1