Automatic Image Enhancement by Content Dependent Exposure Correction
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Automatic Image Enhancement by Content Dependent Exposure Correction S. Battiato University of Catania, Department of Mathematic and Informatics, 95125 Catania, Italy Email: [email protected]
A. Bosco STMicroelectronics, M6 Site, Zona Industriale, 95121 Catania, Italy Email: [email protected]
A. Castorina STMicroelectronics, M6 Site, Zona Industriale, 95121 Catania, Italy Email: [email protected]
G. Messina STMicroelectronics, M6 Site, Zona Industriale, 95121 Catania, Italy Email: [email protected] Received 7 August 2003; Revised 8 March 2004 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. Keywords and phrases: Bayer pattern, skin recognition, features extraction, contrast, focus, exposure correction.
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INTRODUCTION
Reduction of processing time and quality enhancement of acquired images is becoming much more significant. The use of sensors with greater resolution combined with advanced solutions [1, 2, 3, 4] aims to improve the quality of resulting images. One of the main problems affecting image quality, leading to unpleasant pictures, comes from improper exposure to light. Beside the sophisticated features incorporated in today’s cameras (i.e., automatic gain control algorithms), failures are not unlikely to occur. Some techniques are completely automatic, cases in point being represented by those based on “average/automatic exposure metering” or the more complex “matrix/intelligent exposure metering.” Others, again, accord the photographer a certain control over the selection of the exposure, thus allowing space for personal taste or enabling him to satisfy particular needs. Inspite of the great variety of methods [5, 6], for regulating the exposure and the complexity of some of them, it is
not rare for images to be acquired with a nonoptimal or incorrect exposure. This is particularly true for handset devices (e.g., mobile phones) where several factors contribute to acquire bad-exposed pictures: poor optics, absence of flashgun, not to talk about “difficult” input scene lighting conditions, and so forth. There is no exact definition of what a correct exposure should be. It is possible to abstract a generalization and to define the best exposure that enables one to reproduce the most important regions (according to contextual or perceptive criteria) with a level of gray or brightness, more or less in the middle of the possible range. Using postprocessing techniques an effective enhancement should be obtained. Typic
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