Recent advances in mobile phone cameras have poised them to take over compact

hand-held cameras as the consumer’s preferred camera option. Along with

advances in the number of pixels, motion blur removal, face-tracking, and noise reduction

algorithms have significant roles in the internal processing of the devices.

An undesired effect of severe noise reduction is the loss of texture (i.e. low-contrast

fine details) of the original scene. Current established methods for resolution measurement

fail to accurately portray the texture loss incurred in a camera system.

The development of an accurate objective method to identify the texture preservation

or texture reproduction capability of a camera device is important in this

regard.

The ‘Dead Leaves’ target has been used extensively as a method to measure

the modulation transfer function (MTF) of cameras that employ highly non-linear

noise-reduction methods. This stochastic model consists of a series of overlapping

circles with radii r distributed as r−3, and having uniformly distributed gray level,

which gives an accurate model of occlusion in a natural setting and hence mimics

a natural scene. This target can be used to model the texture transfer through a

camera system when a natural scene is captured.

In the first part of our study we identify various factors that affect the MTF

measured using the ‘Dead Leaves’ chart. These include variations in illumination,

distance, exposure time and ISO sensitivity among others. We discuss the main

differences of this method with the existing resolution measurement techniques and

identify the advantages.

In the second part of this study, we propose an improvement to the current texture

MTF measurement algorithm. High frequency residual noise in the processed

image contains the same frequency content as fine texture detail, and is sometimes

reported as such, thereby leading to inaccurate results. A wavelet thresholding based

denoising technique is utilized for modeling the noise present in the final

captured image. This updated noise model is then used for calculating an accurate

texture MTF. We present comparative results for both algorithms under various

image capture conditions.