Physics-based approach to color image enhancement in poor visibility conditions.

Degradation of images by the atmosphere is a familiar problem. For example, when terrain is imaged from a forward-looking airborne camera, the atmosphere degradation causes a loss in both contrast and color information. Enhancement of such images is a difficult task because of the complexity in restoring both the luminance and the chrominance while maintaining good color fidelity. One particular problem is the fact that the level of contrast loss depends strongly on wavelength. A novel method is presented for the enhancement of color images. This method is based on the underlying physics of the degradation process, and the parameters required for enhancement are estimated from the image itself.

Contrast enhanced dynamic MRI of cervical carcinoma during radiotherapy: early prediction of tumour regression rate.

This prospective study investigated the relationship between changes in the MRI dynamic enhancement of cervical carcinoma early during radiotherapy, and tumour regression rate throughout radiotherapy. A total of 36 MRI examinations was performed in seven patients with cervical carcinoma, including a T2 weighted sequence weekly during radiotherapy and also a multislice dynamic Gd-DTPA enhanced sequence before and after the first 2 weeks of radiotherapy. Tumour enhancement was determined on dynamic images using a region of interest and signal-to-noise ratio method. Serial tumour volumes over time on T2 weighted images were estimated using the Cavalieri method of modern design-based stereology to obtain tumour regression rate. It was found that peak and mean enhancement prior to radiotherapy ranged from 3.0 to 13.3, and from 1.9 to 12.2, respectively. After 2 weeks of radiotherapy, peak and mean enhancement ranged from 7.5 to 13.0, and from 6.3 to 10.6, respectively. The change in peak and mean tumour enhancement between dynamic scans ranged, respectively, from -2.0 to 8.4 and from -4.5 to 8.5. Tumour volume decreased exponentially with time (p < 0.01). Tumour regression rates ranged from 2.0% to 15.2% per day, and correlated positively with changes of both peak and mean tumour enhancement (p < 0.01). It is concluded that MRI dynamic enhancement during the first 2 weeks of radiotherapy may provide early prediction of tumour regression rate, and therefore be of value in designing treatment schedules for cervical carcinoma.

Improving image quality in poor visibility conditions using a physical model for contrast degradation.

In daylight viewing conditions, image contrast is often significantly degraded by atmospheric aerosols such as haze and fog. This paper introduces a method for reducing this degradation in situations in which the scene geometry is known. Contrast is lost because light is scattered toward the sensor by the aerosol particles and because the light reflected by the terrain is attenuated by the aerosol. This degradation is approximately characterized by a simple, physically based model with three parameters. The method involves two steps: first, an inverse problem is solved in order to recover the three model parameters; then, for each pixel, the relative contributions of scattered and reflected flux are estimated. The estimated scatter contribution is simply subtracted from the pixel value and the remainder is scaled to compensate for aerosol attenuation. This paper describes the image processing algorithm and presents an analysis of the signal-to-noise ratio (SNR) in the resulting enhanced image. This analysis shows that the SNR decreases exponentially with range. A temporal filter structure is proposed to solve this problem. Results are presented for two image sequences taken from an airborne camera in hazy conditions and one sequence in clear conditions. A satisfactory agreement between the model and the experimental data is shown for the haze conditions. A significant improvement in image quality is demonstrated when using the contrast enhancement algorithm in conjuction with a temporal filter.