Noise Removal in the Developing Process of Digital Negatives. Most modern color digital cameras are equipped with a single image sensor with a color filter array (CFA). One of the most important stages of preprocessing is noise reduction. Most research related to this topic ignores the problem associated with the actual color image acquisition process and assumes that we are processing the image in the sRGB space. In the presented paper, the real process of developing raw images obtained from the CFA sensor was analyzed. As part of the work, a diverse database of test images in the form of a digital negative and its reference version was prepared. The main problem posed in the work was the location of the denoising and demosaicing algorithms in the entire raw image processing pipeline. For this purpose, all stages of processing the digital negative are reproduced. The process of noise generation in the image sensors was also simulated, parameterizing it with ISO sensitivity for a specific CMOS sensor. In this work, we tested commonly used algorithms based on the idea of nonlocal means, such as NLM or BM3D, in combination with various techniques of interpolation of CFA sensor data. Our experiments have shown that the use of noise reduction methods directly on the raw sensor data, improves the final result only in the case of highly disturbed images, which corresponds to the process of image acquisition in difficult lighting conditions.

Film grain noise modeling in advanced video coding A new technique for film grain noise extraction, modeling and synthesis is proposed and applied to the coding of high definition video in this work. The film grain noise is viewed as a part of artistic presentation by people in the movie industry. On one hand, since the film grain noise can boost the natural appearance of pictures in high definition video, it should be preserved in highfidelity video processing systems. On the other hand, video coding with film grain noise is expensive. It is desirable to extract film grain noise from the input video as a preprocessing step at the encoder and resynthesize the film grain noise and add it back to the decoded video as a postprocessing step at the decoder. Under this framework, the coding gain of the denoised video is higher while the quality of the final reconstructed video can still be well preserved. Following this idea, we present a method to remove film grain noise from imagevideo without distorting its original content. Besides, we describe a parametric model containing a small set of parameters to represent the extracted film grain noise. The proposed model generates the film grain noise that is close to the real one in terms of power spectral density and crosschannel spectral correlation. Experimental results are shown to demonstrate the efficiency of the proposed scheme.