Channel (digital image)

About: Channel (digital image) is a research topic. Over the lifetime, 7211 publications have been published within this topic receiving 69974 citations.

Digital photography device and method

Abstract: A combination of digital camera design and digital photography technique that allows a single digital camera to be used for both single-shot and multiple-shot color operation. The invention includes a digital camera half of the light-sensing elements of which--the "majority pixels"--are sensitive to a first primary color and the other half of which--the "minority pixels"--are divided about equally between those sensitive to a second primary color and those sensitive to a third primary color. Further, these light-sensitive elements are arranged so that by a simple lateral shift of the array, typically by the width of a single pixel, every one of the majority pixels will be moved into a position previously held by one of the minority pixels. When used in conjunction with known pixel-interpolation methods this camera produces single-shot digital images equal or better in color quality to any existing single-shot digital camera. Shooting a scene twice, with the array shifted as indicated above between the two shots, and using known interpolation techniques to determine all three primary colors at each pixel location, a final photograph essentially as good as any provided by traditional three-shot digital photography can be achieved. For cameras having the requisite array of photosensitive elements, but not incorporating a mechanism for moving the array separately from the rest of the camera, the present invention can be practiced by rotating the described camera about an axis lying in the principal plane of and passing through the principal point of the camera's lens.

Processing device for correcting defect pixel values of an image sensor unit, image sensor unit with the processing device and method

Abstract: CMOS image sensors are usually suffering from fixed pattern noise and random defect pixels. However, for economical reasons and in order to increase the manufacturing yield, some random defective pixels are usually accepted even for professional devices. In this case, the defect pixels are usually corrected by signal processing. A processing device (15) for correcting of at least one defect pixel value of an image sensor unit is proposed, the image sensor unit comprising at least a first and a second pixel array (1, 2, 3), wherein the image sensor unit is embodied to project the same image onto each pixel array (1, 2, 3), the processing device (15) comprising at least a first and a second input channel (11, 12, 13) for receiving pixel values of the first and the second pixel array, respectively, wherein the processing device (15) is operable to exchange the defect pixel value by a corrected pixel value, wherein the corrected pixel value is estimated by evaluating the values of neighbouring pixels of the defect pixel of the same pixel array, wherein the corrected pixel value is estimated by evaluating values of a corresponding pixel and its neighbouring pixels of the second pixel array at the same location as the defect pixel of the first pixel array in respect to the projected image.

Sparsity-based Color Image Super Resolution via Exploiting Cross Channel Constraints

Abstract: Sparsity constrained single image super-resolution (SR) has been of much recent interest. A typical approach involves sparsely representing patches in a low-resolution (LR) input image via a dictionary of example LR patches, and then using the coefficients of this representation to generate the high-resolution (HR) output via an analogous HR dictionary. However, most existing sparse representation methods for super resolution focus on the luminance channel information and do not capture interactions between color channels. In this work, we extend sparsity based super-resolution to multiple color channels by taking color information into account. Edge similarities amongst RGB color bands are exploited as cross channel correlation constraints. These additional constraints lead to a new optimization problem which is not easily solvable; however, a tractable solution is proposed to solve it efficiently. Moreover, to fully exploit the complementary information among color channels, a dictionary learning method is also proposed specifically to learn color dictionaries that encourage edge similarities. Merits of the proposed method over state of the art are demonstrated both visually and quantitatively using image quality metrics.