ipa: raspberrypi: alsc: Limit the calculated lambda values

Under the right circumstances, the alsc calculations could spread the colour
errors across the entire image as lambda remains unbound. This would cause the
corrected image chroma values to slowly drift to incorrect values.

This change adds a config parameter (alsc.lambda_bound) that provides an upper
and lower bound to the lambda value at every stage of the calculation. With this
change, we now adjust the lambda values so that the average across the entire
grid is 1 instead of normalising to the minimum value.

Signed-off-by: Naushir Patuck <naush@raspberrypi.com>
Reviewed-by: David Plowman <david.plowman@raspberrypi.com>
Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
Tested-by: Naushir Patuck <naush@raspberrypi.com>
Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>

2 files changed, 44 insertions, 15 deletions

diff --git a/src/ipa/raspberrypi/controller/rpi/alsc.cpp b/src/ipa/raspberrypi/controller/rpi/alsc.cpp
index be3d1ae4..e575c14a 100644
--- a/src/ipa/raspberrypi/controller/rpi/alsc.cpp
+++ b/src/ipa/raspberrypi/controller/rpi/alsc.cpp
@@ -4,9 +4,12 @@
  *
  * alsc.cpp - ALSC (auto lens shading correction) control algorithm
  */
+
 #include <math.h>
+#include <numeric>
 
 #include <libcamera/base/log.h>
+#include <libcamera/base/span.h>
 
 #include "../awb_status.h"
 #include "alsc.hpp"
@@ -149,6 +152,7 @@ void Alsc::Read(boost::property_tree::ptree const &params)
 	read_calibrations(config_.calibrations_Cb, params, "calibrations_Cb");
 	config_.default_ct = params.get<double>("default_ct", 4500.0);
 	config_.threshold = params.get<double>("threshold", 1e-3);
+	config_.lambda_bound = params.get<double>("lambda_bound", 0.05);
 }
 
 static double get_ct(Metadata *metadata, double default_ct);
@@ -610,30 +614,47 @@ static double compute_lambda_top_end(int i, double const M[XY][4],
 
 // Gauss-Seidel iteration with over-relaxation.
 static double gauss_seidel2_SOR(double const M[XY][4], double omega,
-				double lambda[XY])
+				double lambda[XY], double lambda_bound)
 {
+	const double min = 1 - lambda_bound, max = 1 + lambda_bound;
 	double old_lambda[XY];
 	int i;
 	for (i = 0; i < XY; i++)
 		old_lambda[i] = lambda[i];
 	lambda[0] = compute_lambda_bottom_start(0, M, lambda);
-	for (i = 1; i < X; i++)
+	lambda[0] = std::clamp(lambda[0], min, max);
+	for (i = 1; i < X; i++) {
 		lambda[i] = compute_lambda_bottom(i, M, lambda);
-	for (; i < XY - X; i++)
+		lambda[i] = std::clamp(lambda[i], min, max);
+	}
+	for (; i < XY - X; i++) {
 		lambda[i] = compute_lambda_interior(i, M, lambda);
-	for (; i < XY - 1; i++)
+		lambda[i] = std::clamp(lambda[i], min, max);
+	}
+	for (; i < XY - 1; i++) {
 		lambda[i] = compute_lambda_top(i, M, lambda);
+		lambda[i] = std::clamp(lambda[i], min, max);
+	}
 	lambda[i] = compute_lambda_top_end(i, M, lambda);
+	lambda[i] = std::clamp(lambda[i], min, max);
 	// Also solve the system from bottom to top, to help spread the updates
 	// better.
 	lambda[i] = compute_lambda_top_end(i, M, lambda);
-	for (i = XY - 2; i >= XY - X; i--)
+	lambda[i] = std::clamp(lambda[i], min, max);
+	for (i = XY - 2; i >= XY - X; i--) {
 		lambda[i] = compute_lambda_top(i, M, lambda);
-	for (; i >= X; i--)
+		lambda[i] = std::clamp(lambda[i], min, max);
+	}
+	for (; i >= X; i--) {
 		lambda[i] = compute_lambda_interior(i, M, lambda);
-	for (; i >= 1; i--)
+		lambda[i] = std::clamp(lambda[i], min, max);
+	}
+	for (; i >= 1; i--) {
 		lambda[i] = compute_lambda_bottom(i, M, lambda);
+		lambda[i] = std::clamp(lambda[i], min, max);
+	}
 	lambda[0] = compute_lambda_bottom_start(0, M, lambda);
+	lambda[0] = std::clamp(lambda[0], min, max);
 	double max_diff = 0;
 	for (i = 0; i < XY; i++) {
 		lambda[i] = old_lambda[i] + (lambda[i] - old_lambda[i]) * omega;
@@ -653,15 +674,24 @@ static void normalise(double *ptr, size_t n)
 		ptr[i] /= minval;
 }
 
+// Rescale the values so that the average value is 1.
+static void reaverage(Span<double> data)
+{
+	double sum = std::accumulate(data.begin(), data.end(), 0.0);
+	double ratio = 1 / (sum / data.size());
+	for (double &d : data)
+		d *= ratio;
+}
+
 static void run_matrix_iterations(double const C[XY], double lambda[XY],
 				  double const W[XY][4], double omega,
-				  int n_iter, double threshold)
+				  int n_iter, double threshold, double lambda_bound)
 {
 	double M[XY][4];
 	construct_M(C, W, M);
 	double last_max_diff = std::numeric_limits<double>::max();
 	for (int i = 0; i < n_iter; i++) {
-		double max_diff = fabs(gauss_seidel2_SOR(M, omega, lambda));
+		double max_diff = fabs(gauss_seidel2_SOR(M, omega, lambda, lambda_bound));
 		if (max_diff < threshold) {
 			LOG(RPiAlsc, Debug)
 				<< "Stop after " << i + 1 << " iterations";
@@ -675,10 +705,8 @@ static void run_matrix_iterations(double const C[XY], double lambda[XY],
 				<< last_max_diff << " to " << max_diff;
 		last_max_diff = max_diff;
 	}
-	// We're going to normalise the lambdas so the smallest is 1. Not sure
-	// this is really necessary as they get renormalised later, but I
-	// suppose it does stop these quantities from wandering off...
-	normalise(lambda, XY);
+	// We're going to normalise the lambdas so the total average is 1.
+	reaverage({ lambda, XY });
 }
 
 static void add_luminance_rb(double result[XY], double const lambda[XY],
@@ -737,9 +765,9 @@ void Alsc::doAlsc()
 	compute_W(Cb, config_.sigma_Cb, Wb);
 	// Run Gauss-Seidel iterations over the resulting matrix, for R and B.
 	run_matrix_iterations(Cr, lambda_r_, Wr, config_.omega, config_.n_iter,
-			      config_.threshold);
+			      config_.threshold, config_.lambda_bound);
 	run_matrix_iterations(Cb, lambda_b_, Wb, config_.omega, config_.n_iter,
-			      config_.threshold);
+			      config_.threshold, config_.lambda_bound);
 	// Fold the calibrated gains into our final lambda values. (Note that on
 	// the next run, we re-start with the lambda values that don't have the
 	// calibration gains included.)
diff --git a/src/ipa/raspberrypi/controller/rpi/alsc.hpp b/src/ipa/raspberrypi/controller/rpi/alsc.hpp
index 9616b99e..d1dbe0d1 100644
--- a/src/ipa/raspberrypi/controller/rpi/alsc.hpp
+++ b/src/ipa/raspberrypi/controller/rpi/alsc.hpp
@@ -41,6 +41,7 @@ struct AlscConfig {
 	std::vector<AlscCalibration> calibrations_Cb;
 	double default_ct; // colour temperature if no metadata found
 	double threshold; // iteration termination threshold
+	double lambda_bound; // upper/lower bound for lambda from a value of 1
 };
 
 class Alsc : public Algorithm