Camera spectral sensitivity (CSS) establishes the connection between scene radiance and device-captured RGB tristimulus values. Since the spectral sensitivity of most color imaging devices typically deviates from that of human vision or a standard color space and also noise is often introduced during the process of photoelectric signal conversion and transmission, the design of an efficient CSS with noise robustness and high color fidelity is of paramount importance. In this paper, we propose a CSS optimization method with noise consideration that designs theoretically an optimal CSS for each noise level. Additionally, taking practical considerations into account, we further extend the proposed method for a universally optimal CSS adaptable to diverse noise levels. Experimental results show that our optimized CSS is more robust to noise and has better imaging performance than existing optimization methods based on a fixed CSS. The source code is available at https://github.com/xyu12/Joint-Design-of-CSS-and-CCM-with-Noise-Consideration-EI2024.
A similarity search in images has become a typical operation in many applications. A presence of noise in images greatly affects the correctness of detection of similar image blocks, resulting in a reduction of efficiency of image processing methods, e.g., non-local denoising. In this paper, we study noise immunity of various distance measures (similarity metrics). Taking into account a wide variety of information content in real life images and variations of noise type and intensity. We propose a set of test data and obtain preliminary results for several typical cases of image and noise properties. The recommendations for metrics' and threshold selection are given. Fast implementation of the proposed benchmark is realized using CUDA technology.