We describe an automatic image enhancement technique based on features extraction methods. The approach takes into account images in Bayer data format, captured using a CCD/CMOS sensor and/or 24-bit color images; after identifying the visually significant features, the algorithm adjusts the exposure level using a camera response-like function; then a final HUE reconstruction is achieved. This method is suitable for handset devices acquisition systems (e.g., mobile phones, PDA, etc.). The process is also suitable to solve some of the typical drawbacks due to several factors such as poor optics, absence of flashgun, and so forth.
This paper presents a spatial noise reduction technique designed to work on CFA (Color Filtering Array) data acquired by CCD/CMOS image sensors. The overall processing preserves image details using some heuristics related to the HVS (Human Visual System); estimates of local texture degree and noise levels are computed to regulate the filter smoothing capability. Experimental results confirm the effectiveness of the proposed technique. The method is also suitable for implementation in low power mobile devices with imaging capabilities such as camera phones and PDAs.
The proposed paper concerns the processing of images in digital format and, more specifically, particular techniques that can be advantageously used in digital still cameras for improving the quality of images acquired with a non-optimal exposure. The proposed approach analyses the CCDICMOS sensor Bayer data or the corresponding color generated image and, after identifying specific features, it adjusts the exposure level according to a 'camera response' like function.
The paper presents a new and statistical robust algorithm able to improve the performance of the standard DCT compression algorithm for both perceived quality and compression size. The approach proposed combines together an information theoretical/statistical approach with HVS (Human Visual System) response functions. The methodology applied permits to obtain suitable quantization table for specific classes of images and specific viewing conditions. The paper presents a case study where the right parameters are learned, ajer an extensive experimental phase, for three specific classes: Document, Landscape and Portrait. The results show both perceptive and measured (in term of PSNR) improvement. A further application shows how it is possible obtain significative improvement profiling the relative DCT error inside the pipeline of images acquired by typical digital sensors.
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