Abstract:
The level of Gaussian noise in a memory field being scanned by rows is reduced by reconstructing each pixel by fuzzy logic processors, the latter processing the the values of pixels neighbouring the pixel being processed and belonging to a processing window defined by the last scanned row and the row being scanned, thus minimizing the memory requisite of the filtering system to a single row. The system perform an adaptive filtering within the current field itself and does not produce edge-smoothing effects as in prior adaptive filtering systems operating on consecutive fields.
Abstract:
Digital photography apparatus (100), particularly a digital still camera, comprising means (110-125) for acquiring a digital image, compression means (130) for obtaining a compressed image, and a memory (135) for storing the compressed image, the apparatus also including processing means (140) for obtaining a processed image and corresponding processing parameters from the image acquired, the processing means (140) supplying as an output, in a first operative condition, the processed image to be compressed by the compression means (130) and, in a second operative condition, the image acquired to be compressed by the compression means (130) and the processing parameters to be stored in the memory (135).
Abstract:
A method and a device for motion estimated and compensated Field Rate Up-conversion (FRU) for video applications, providing for: a) dividing an image field to be interpolated into a plurality of image blocks (IB), each image block made up of a respective set of image elements of the image field to be interpolated; b) for each image block (K(x,y)) of at least a sub-plurality (Q1,Q2) of said plurality of image blocks, considering a group of neighboring image blocks (NB[1]-NB[4]); c) determining an estimated motion vector for said image block (K(x,y)), describing the movement of said image block (K(x,y)) from a previous image field to a following image field between which the image field to be interpolated is comprised, on the basis of predictor motion vectors (P[1]-P[4]) associated to said group of neighboring image blocks; d) determining each image element of said image block (K(x,y)) by interpolation of two corresponding image elements in said previous and following image fields related by said estimated motion vector. Step c) provides for: c1) applying to the image block (K(x,y)) each of said predictor motion vectors to determine a respective pair of corresponding image blocks in said previous and following image fields, respectively; c2) for each of said pairs of corresponding image blocks, evaluating an error function (err[i]) which is the Sum of luminance Absolute Difference (SAD) between corresponding image elements in said pair of corresponding image blocks; c3) for each pair of said predictor motion vectors, evaluating a degree of homogeneity (H(i,j)); c4) for each pair of said predictor motion vectors, applying a fuzzy rule having an activation level (r[k]) which is higher the higher the degree of homogeneity of the pair of predictor motion vectors and the smaller the error functions of the pair of predictor motion vectors; c5) determining an optimum fuzzy rule having the highest activation level (r[opt]), and determining the best predictor motion vector (P[min]) of the pair associated to said optimum fuzzy rule having the smaller error function; c6) determining the estimated motion vector for said image block (K(x,y)) on the basis of said best predictor motion vector (P[min]).