A major obstacle in photography is the presence of distracting elements that pull attention away from the main subject and clutter the composition. Photographers have developed post-processing techniques to reduce the salience of distractors by altering low-level features to which the visual system is particularly attuned: sharpness, brightness, chromaticity, or saturation. Biologically-inspired models of attention identify salient regions as statistical outliers in these feature distributions. One low-level feature that cannot be directly manipulated with existing image-editing software is texture variation. Psychophysical studies have shown that discontinuities in texture can elicit an edge perception similar to that triggered by color discontinuities. First-order computational models of saliency measure the response to filter banks that extract contrast and orientation in the image. Various non-linearities can then be used to extract and combine maxima of the response to each feature. A recently-introduced secondorder model performs additional image processing on the response to a first-order filter bank, effectively performing the same computation as first-order models but on what we term power maps rather than on image intensity. Higher-order features describing local frequency content, power maps have been used previously in image analysis; e.g. response to multiscale oriented filters can be used for texture discrimination. We show that power maps are also a powerful representation for manipulating frequency content in an image. We introduce an image-processing technique for selectively reducing spatial variation of texture to reduce the salience of distracting regions. In a nutshell, our texture equalization technique modifies distracting regions to make them look more like uniform textures. We illustrate our technique with a 1D example. The input signal ( Fig. 2(a) ) is first band-pass filtered (b) and rectified with an absolute value non-linearity (c). For the 2D case, we use steerable pyramid filters to compute frequency content because they permit straightforward analysis, processing, and reconstruction of images. Pooling the rectified response by applying a low-pass filter with a Gaussian kernel captures the local frequency content. We call the resulting image the power map (d). To reduce texture variation in the image, some portion of the high frequencies of the power maps must be removed, a seemingly trivial image-processing operation. However, we must define how a modification of the power map translates into a modification of pyramid coefficients. The exponent of the high-pass response (e) is used to scale the bandpass response. Because the goal is to reduce variation, a negative multiple of the high-pass is used as the scale factor. Note how the scaled signal (f) has been 'flattened' compared to the input. In the 2D case, the scaled subbands are then recombined to produce the final texture-equalized image.  To validate our technique's effectiveness, we used an eye tracker to record qualitative changes in user fixations on original and modified images. We found that emphasized regions attracted and held fixations longer than de-emphasized ones. Results of a search experiment quantified the effect of our technique on response time. Subjects were asked to find a target object in a series of images, some unmodified and some in which distractors had been de-emphasized. Texture equalization resulted in a search speedup of more than 20%. Texture equalization is complementary to de-emphasis methods such as Gaussian blur, which increases depth-of-field effects. Reduced sharpness can be undesirable, particularly if distractors are at the same distance as the main subject. Blur removes high frequencies and emphasizes medium ones, possibly resulting in a more distracting object, while our technique makes high-frequencies more uniform, "camouflaging" medium-frequency content. Our technique is most effective for textured image regions, while Gaussian blur works best when small depth-of-field effects are already present and when medium-frequency content is not distracting. Please see the full-resolution color images at http://csail.mit.edu/˜sarasu/pub/apgv05 164
One of the earlier works REF suggested a method for reducing the background saliency, thus implicitly enhancing the saliency of the main foreground object.